The Internet has become increasingly popular technology for product purchases (E-commerce) and products researching. Typically, users of the Internet, prior to purchasing a product either online or through a traditional vendor, will research the product using various search tools. Internet users may utilize search tools for a variety of reasons. For example, to research the attributes of a particular product, locations of where to purchase the particular product, or alternative products that have the same attributes as the focal product.
In one example, a user purchasing a car may first research the car on the Internet, where the user may have a particular car in mind and simply desire to determine the best price or the best location of where to purchase the car. However, the user may have a particular car in mind, but desire to view alternative cars having similar attributes as the focal car or having different attributes than the focal car. Researching products, such as a car, having many decision points or criteria, may be a difficult and time-consuming process. In the example of a car, various criteria include, but are not limited to, discrete criteria such as seating capacity, number of doors, average miles per gallon (MPG), manufacturer's specified retail price (MSRP), and curb weight, for example. Further complicating the research and decision process are various criteria that are general in nature such as performance, economy, comfort, and safety.
Current technology lacks the tools to quickly and efficiently enable a purchaser of a product to determine what product best fits the wants and needs of the purchaser. It also would be desirable to have a tool that helps the user determine which criteria are of most importance to them as well as determine the relative importance of each criteria within a respective data set. When deciding between items with many discrete attributes, it becomes necessary to group some into larger aggregate criteria; for example aggregating front leg room, front head room, heated seats, and lumbar support into a “comfort” criteria for cars. Currently there is not an efficient way to call out differences between products or categories. For example, given differences in various vehicle attributes, there are no current methods to determine which are most significant.